Recognizing the ripeness of bananas using artificial neural network based on histogram approach

Saad, Hasnida; Ismail, Ahmad Puad; Othman, Norinsan Kamil; Jusoh, Mohamad Huzaimy; Naim, Nani Fadzlina; Ahmad, N. A.

doi:10.1109/icsipa.2009.5478715

Cited by 17 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A single banana is called a finger, and a grouping of attached fingers of 10 or more bananas is called a hand [ 3 ]. In this review, out of the 35 studies, a total of 27 [ 3 , 4 , 5 , 6 , 9 , 10 , 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] have used the fingers of bananas, 3 [ 37 , 38 , 39 ] studies have used the fingers by hand and Chen et al [ 40 ] used a bunch of bananas. Mohamedon et al and Rodrigues et al [ 41 , 42 ] used a mixed sample of bananas, Mendoza and Aguilera [ 43 ] used the combination of fingers by hand and a single batch of bananas, and whereas Saragih and Emanuel [ 44 ] also used the used the combination of fingers and fingers and hand bananas.…”

Section: Resultsmentioning

confidence: 99%

“…Apart from that, three [ 6 , 9 , 25 ] studies used the Egyptian species, and Sabilla et al [ 21 ] used local bananas from traditional markets in Indonesia (Pisang Emas, Pisang Kepok, Pisang Ambon, Pisang Raja Nangka, Pisang Santen, Pisang Susu, and Pisang Candi). Likewise, Saragih and Emanuel’s [ 44 ] study used the combination of two categories (Egyptian species and an unreported category), while the remaining 10 [ 11 , 18 , 24 , 26 , 30 , 34 , 37 , 38 , 41 , 42 ] studies did not report their banana categories.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Baglat

Hayat

Mendonça

et al. 2023

Sensors

View full text Add to dashboard Cite

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Baglat

Hayat

Mendonça

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Image analysis in different color spaces has been used before for quantifying the ripening process in bananas. Color space Red, Green, and Blue (RGB) representation of banana image was used with an artificial neural network (ANN) to build automatic classification systems [11,12].…”

Section: Stagementioning

confidence: 99%

“…Other techniques are not considered for comparison because they depend on the extraction of some features from a specific channel in color spaces (i.e., RGB images for generating an automated classification system [11,12]; brown area percentage in CIE L * a * b * [13]; ripening color index from the channels a * b * from CIE L * a * b * [14]; fuzzy system of images in two color spaces, HSV and CIE L * a * b * [15]). In all these cases, a three-channel image representation is needed.…”

Section: Texture Analysis Computing the Homogeneity Criteriamentioning

confidence: 99%

Nondestructive Quantification of the Ripening Process in Banana (Musa AAB Simmonds) Using Multispectral Imaging

et al. 2019

View full text Add to dashboard Cite

The ripening process in bananas causes the waste of a significant part of the production of this fruit. The aim of this research is to find a new technique useful for identifying, registering, and quantifying the ripening process of a banana (Musa AAB Simmonds) at the seventh stage of the growing process. This quantification is proposed with a nondestructive technique based on processing multispectral images. This experiment used a set of multispectral imagery registered in a range of 270-1000 nm (from UV to IR) with the aid of a monochromatic camera and a set of 10 optical filters. Multispectral images were analyzed with three different techniques: Fourier fractal analysis, Hotelling transform, and homogeneity texture analysis based on cooccurrence matrix. First, a characteristic index was computed for each technique for a daily set of multispectral imagery. These indexes are slope index, for Fourier fractals; the average of the computed eigenvalues, with Hotelling transform; and the texture homogeneity value. These indexes were evaluated using the behavior of the resulting graphs for a seven-day period, being preferred those graphs with a tendency of decreasing values. Finally, the repeatability of each technique was evaluated by reproducing similar values for each day during the evaluation period. These three methods will be compared in this article in order to select the one with the best performance for measuring the ripening process in bananas. The obtained results show that it is possible to effectively isolate the brown spots from the banana peel with the Hotelling transform by using only 2 optical filters: visible (410-690 nm) and Near-IR (820-910 nm). With the resulting spectral image fusion it was possible to effectively describe the evolution of the brown spots present in the ripening process through the texture homogeneity criteria.

show abstract

“…In Deep Learning, histograms appear in different contexts: they can be used as neural network (NN) inputs (e.g. Saadl et al 2009;Rebetez et al 2016), different variants of hidden histogram layers have been proposed (Wang et al, 2016;Sedighi & Fridrich, 2017;Peeples et al, 2020), block-wise histograms have been employed for feature pooling in Chan et al (2015), and histogram loss functions were introduced in Ustinova & Lempitsky (2016); Zholus & Putin (2020). Furthermore, histograms of the trainable NN weights in different layers can shed light on whether the NN training is progressing properly.…”

Section: Introductionmentioning

confidence: 99%

The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression

List

2021

Preprint

View full text Add to dashboard Cite

Although ubiquitous in the sciences, histogram data have not received much attention by the Deep Learning community. Whilst regression and classification tasks for scalar and vector data are routinely solved by neural networks, a principled approach for estimating histogram labels as a function of an input vector or image is lacking in the literature. We present a dedicated method for Deep Learning-based histogram regression, which incorporates cross-bin information and yields distributions over possible histograms, expressed by τ -quantiles of the cumulative histogram in each bin. The crux of our approach is a new loss function obtained by applying the pinball loss to the cumulative histogram, which for 1D histograms reduces to the Earth Mover's distance (EMD) in the special case of the median (τ = 0.5), and generalizes it to arbitrary quantiles. We validate our method with an illustrative toy example, a football-related task, and an astrophysical computer vision problem. We show that with our loss function, the accuracy of the predicted median histograms is very similar to the standard EMD case (and higher than for perbin loss functions such as cross-entropy), while the predictions become much more informative at almost no additional computational cost.

show abstract

Recognizing the ripeness of bananas using artificial neural network based on histogram approach

Cited by 17 publications

References 3 publications

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Nondestructive Quantification of the Ripening Process in Banana (Musa AAB Simmonds) Using Multispectral Imaging

The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression

Contact Info

Product

Resources

About